Active noise control system and method

ABSTRACT

An Active Noise Control (ANC) for controlling a noise produced by a noise source may include an acoustic sensor ( 212 ) to sense a noise pattern and to produce a noise signal corresponding to the sensed noise pattern, an estimator ( 202 ) to produce a predicted noise signal by applying an estimation function to the noise signal, and an acoustic transducer ( 216 ) to produce a noise destructive pattern based on the predicted noise signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a US National Phase of PCT Application No.PCT/IL2004/000863, filed on Sep. 19, 2004, which claims the benefitunder 35 U.S.C. 119(e) of US Provisional Application No. 60/503,471filed Sep. 17, 2003 and is a continuation-in-part of U.S. applicationSer. No. 09/120,973 filed Jul. 22, 1998 which claims benefit fromIsraeli Application 121555 filed Aug. 14, 1997, the disclosure of whichis incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to the field of active noise control.

BACKGROUND

Conventional passive noise control systems may include “insulation”elements, silencers, vibration mounts, damping treatments, absorptivetreatments, e.g., ceiling tiles, and/or conventional mufflers, e.g.,mufflers as may be used in the automobile industry. The dimensionsand/or mass of such passive noise control systems may usually depend onthe acoustic pattern length of the noise intended to be reduced.Generally, passive noise control systems implemented to reduce noises ofrelatively low frequencies are bulky, large, heavy and/or expensive.

SUMMARY

According to embodiments of the invention, Active Noise Control (ANC)may be used to reduce noise energy and wave amplitude of a source noisepattern via an ANC sound system, which produces a noise-destructivepattern related to the source noise pattern such that a reduced noisezone may be created.

According to an exemplary embodiment of the invention, the ANC systemmay include an acoustic sensor, e.g., a microphone, to sense a noisepattern and to produce a noise signal corresponding to the sensed noisepattern; an estimator to produce a predicted noise signal by applying anestimation function to the noise signal; and an acoustic transducer,e.g., a speaker, to produce a noise destructive pattern based on thepredicted noise signal.

According to some exemplary embodiments of the invention, the estimationfunction may include a non-linear estimation function, e.g., a radialbasis function.

The estimator may be able to adapt one or more parameters of theestimation function based on a noise error at a predetermined location.For example, the ANC system may include an error evaluator to evaluatethe noise error based on the noise signal and the predicted noisesignal. Additionally or alternatively, the system may include an errorsensing acoustic sensor to sense the noise error at the predeterminedlocation.

The error evaluator may include a speaker transfer function module toproduce an estimation of the noise destructive pattern, e.g., byapplying a speaker transfer function to the predicted noise signal; amodulation transfer function module to produce an estimation of thenoise pattern at the predetermined location, e.g., by applying amodulation transfer function to the noise signal; and a subtractor tosubtract the estimation of the noise destructive pattern from theestimation of the noise pattern.

According to some exemplary embodiments, the estimator may be able toadapt the one or more parameters based on a predetermined criterion. Forexample, the estimator may be able to reduce, e.g., minimize, the errorvalue by adapting the one or more parameters.

According to another exemplary embodiment of the invention, the ANCsystem may include a primary acoustic sensor, e.g., a microphone, tosense a noise pattern and to produce a corresponding primary noisesignal; at least one secondary acoustic sensor, e.g., microphone, tosense a residual noise pattern and to produce at least one secondarynoise signal corresponding to the residual noise pattern sensed by theat least one secondary microphone, respectively, wherein the at leastone secondary acoustic sensor is separated from the noise source by adistance larger than a distance between the primary acoustic sensor andthe noise source; and a controller to control an acoustic transducer toproduce a noise destructive pattern based on the primary noise signaland the at least one secondary noise signal.

The controller may include, for example, a primary estimator to producea predicted primary signal, e.g., by applying a primary estimationfunction to the primary noise signal; and at least one secondaryestimator to produce at least one predicted secondary signal by applyingat least one secondary estimation function to the at least one secondarynoise signal, respectively.

The primary estimator may be able, for example, to iteratively adapt oneor more parameters of the primary estimation function based on a noiseerror. The at least one secondary estimator may be able, for example, toiteratively adapt one or more parameters of the at least one secondaryestimation function, respectively, based on the noise error.

The controller may control the acoustic transducer based on acombination of the predicted primary signal and the at least onepredicted secondary signal.

BREIF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanied drawings in which:

FIG. 1 is a schematic illustration of an active noise control systemaccording to an exemplary embodiment of the invention;

FIG. 2 is a schematic illustration of a controller according to someexemplary embodiments of the invention that may be used, for example, inconjunction with the system of FIG. 1;

FIG. 3 is a schematic illustration of an active noise control systemaccording to another exemplary embodiment of the invention; and

FIG. 4 is a schematic illustration of a controller according to otherexemplary embodiments of the invention that may be used, for example, inconjunction with the system of FIG. 3.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the drawings have not necessarily been drawnaccurately or to scale. For example, the dimensions of some of theelements may be exaggerated relative to other elements for clarity orseveral physical components included in one functional block or element.Further, where considered appropriate, reference numerals may berepeated among the drawings to indicate corresponding or analogouselements. Moreover, some of the blocks depicted in the drawing may becombined into a single function.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those of ordinary skill in the artthat the present invention may be practiced without these specificdetails. In other instances, well-known methods, procedures, componentsand circuits may not have been described in detail so as not to obscurethe present invention.

According to embodiments of the invention, Active Noise Control (ANC)may be used to reduce noise energy and wave amplitude of a source noisepattern, e.g., including one or more acoustic waves, via an ANC soundsystem, which produces a noise-destructive pattern, e.g., including oneor more acoustic waves, related to the source noise pattern such that areduced noise zone may be created.

Embodiments of the invention include ANC systems and methods, which maybe efficiently implemented for reducing undesirable noises, e.g., atleast noises of generally low frequencies, as described below.

Certain aspects of ANC methods and systems, in accordance with someexemplary embodiments of the invention, are described in U.S. patentapplication Ser. No. 09/120,973, filed Jul. 22, 1998, entitled “ACTIVEACOUSTIC NOISE REDUCTION SYSTEM”; and in European Patent Application02023483.7, filed Oct. 21, 2002, entitled “ACTIVE ACOUSTIC NOISEREDUCTION SYSTEM”, and published Apr. 28, 2004 as publication number1414021. The entire disclosure of both of these applications isincorporated herein by reference.

Reference is made to FIG. 1, which schematically illustrates an ANCsystem 100 according to an exemplary embodiment of the invention.

ANC system 100 may include, for example, a acoustic sensor, e.g., amicrophone 102, denoted MIC1, to sense the noise energy and/or waveamplitude of a noise pattern produced by a noise source 104. Microphone102 may include any suitable microphone able to generate an output noisesignal 103, corresponding to the noise pattern sensed by microphone 112.For example, microphone 102 may include microphone Part No. ECM6AP,available from ARIO Electronics Co. Ltd., Taoyuan, Taiwan. Noise signal103 may include, for example, a sequence of N samples per second. Forexample, N may be 1000 samples per second, e.g., if microphone 103operates at a sampling rate of about 10 KHz.

ANC system 100 may also include an acoustic transducer, e.g., a speaker108, and a controller 106 to control speaker 108 to produce a noisedestructive pattern to reduce or cancel the noise energy and/or waveamplitude of the noise pattern, e.g., within a reduced-noise zone 110,as described in detail below. Speaker 108 may include any suitablespeaker, e.g., as is known in the art. For example, speaker 108 mayinclude speaker Part No. AI 4.0, available from Cerwin-Vega Inc.,Chatsworth, Calif.

According to some exemplary embodiments of the invention, controller 106may be able to evaluate a noise error corresponding to an anticipateddestructive interference between the noise pattern and the noisedestructive pattern at a predetermined location 112 within zone 110, asdescribed below. The noise error may be evaluated, for example, bycontroller 106, e.g., based on noise signal 103, as described below.Additionally or alternatively, the noise error may be sensed by anerror-sampling microphone positioned at the predetermined location, asdescribed below. Controller 106 may control speaker 108 to produce thenoise destructive pattern, e.g., based on noise signal 103 and/or on theevaluated noise error, as described below.

According to some exemplary embodiments of the invention, it may bedesired to control the timing at which the noise destructive pattern isproduced, e.g., in order to efficiently control, e.g., reduce, the noisewithin zone 110. For example, it may be desired to controllably time thenoise destructive pattern corresponding to a sample of the noise patternsuch that the destructive noise pattern reaches a location within zone110, e.g., location 112, at substantially the same time the samplednoise pattern reaches the same location.

According to embodiments of the invention, there may be a time delaybetween the time at which a currently sampled noise pattern reacheslocation 112 and the time at which the noise destructive patterncorresponding to the current sample of the noise pattern reacheslocation 112. This time delay may result, for example, from the timerequired for microphone 102 to sense the noise pattern, the timerequired for controller to process noise signal 103, and/or the timerequired for speaker 108 to produce the noise destructive pattern.

Thus, according to some exemplary embodiments of the invention,controller 106 may estimate a sample of the noise pattern succeeding thecurrent sample (“the succeeding sample”) based on the current sampleand/or one or more previous samples of the noise pattern. Controller 118may provide an input to speaker 113, such that speaker 113 produces thenoise destructive pattern based on the estimated succeeding sample,e.g., such that the noise destructive pattern may reach location 112substantially at the same time the noise pattern reaches location 112.

An acoustic pattern, e.g., the noise pattern, may be characterized by agenerally non-linear function. Thus, according to exemplary embodimentsof the invention, controller 106 may use non-linear estimation toestimate the succeeding sample. Such non-linear estimation may provide,according to exemplary embodiments of the invention, a better estimationof the succeeding sample compared to a corresponding linear estimation.However, according to other embodiments of the invention, controller 106may use any other suitable estimation, e.g., a linear estimation, toestimate the succeeding sample.

According to exemplary embodiments of the invention, controller 106 mayinclude an estimator 121 to produce a predicted noise signal 114 byapplying an estimation function to one or more samples of noise signal103. Speaker 113 may produce the noise destructive pattern based onpredicted noise signal 114, as described below.

Reference is made to FIG. 2, which schematically illustrates acontroller 200 according to some exemplary embodiments of the invention.Although the invention is not limited in this respect, controller 200may be implemented by ANC system 100 (FIG. 1).

According to exemplary embodiments of the invention, controller 200 mayinclude an estimator 202 to receive from an acoustic sensor, e.g., amicrophone 212, a noise signal 210, e.g., including a plurality ofsamples of a sensed noise pattern. Estimator 202 may generate apredicted noise signal 230 having a value, y(n), corresponding to ann-th sample, denoted MIC(n), received from microphone 212, by applyingan estimation function F to the sample MIC(n) and to one or more othersamples previously received from microphone 212, as described below.Controller 202 may control an acoustic transducer, e.g., a speaker 216,to generate a noise destructive pattern 218, e.g., based on output 230.

According to some exemplary embodiments of the invention, estimator 202may implement a non-linear estimation algorithm, as described below.

According to some exemplary embodiments of the invention, estimator 202may implement a Radial Basis Function (RBF) algorithm, as describedbelow.

Estimator 202 may implement the RBF algorithm to estimate the value of asucceeding sample of the noise signal based on the values of one or moresamples of the noise signal received from microphone 212. For example,the RBF algorithm may correspond to a combination of a set of K radialn-dimension functions, wherein each function may differ in one or moreparameters, e.g., a center of the function parameter, denoted c_(k), aneffective radius parameter, denoted v_(k), and/or and intensity of thefunction, denoted w_(k), as are known in the art. For example, estimator202 may implement a RBF algorithm analogous to the one described by S.Haykin, “Adaptive Filter Theory”, 3^(rd) edition, Prentice Hall, pp.863-565.

According to some exemplary embodiments of the invention, estimator 202may generate predicted noise 230 according to the following equation:

$\begin{matrix}{{y\lbrack n\rbrack} = {\sum\limits_{k = 1}^{K}{w_{k}{\exp( {{- \frac{1}{2v_{k}}}{\sum\limits_{i = 0}^{L - 1}( {{{MIC}\lbrack {n - i} \rbrack} - {c_{k}\lbrack i\rbrack}} )^{2}}} )}}}} & (1)\end{matrix}$

wherein L denotes a determined number of samples of the noise signal tobe implemented for the estimation of y(n).

According to some exemplary embodiments of the invention, estimator 202may iteratively adapt one or more parameters, e.g., one or more of theparameters c_(k), v_(k), and w_(k), of the estimation function F, e.g.,based on a predetermined criterion, as described below.

According to some exemplary embodiments of the invention, estimator 202may iteratively adapt one or more of the parameters c_(k), v_(k), andw_(k) based on the evaluated noise error at a predetermined location,e.g., location 112 (FIG. 1), as described below.

According to some exemplary embodiments of the invention, controller 200may also include an error evaluation module 203 to evaluate the noiseerror, e.g., based on noise signal 210 and predicted noise signal 230,as described below.

According to some exemplary embodiments of the invention, module 203 mayinclude, for example, a Modulation Transfer Function (MTF) module 204 toapply to noise signal 210 a predetermined MTF, thereby to generate anoutput 241 having a value corresponding to an estimation, denoted d(n),of the n-th sample of the noise pattern at the predetermined location.The MTF may be determined, for example, based on characteristics ofmicrophone 212 and/or based on geometrical and/or physicalcharacteristics of a path and/or a medium, e.g., air, between microphone212 and the predetermined location, e.g., as known in the art. MTFmodule 204 may include any suitable hardware and/or software, e.g., asare known in the art, to apply a predetermined MTF to noise signal 210.

According to exemplary embodiments of the invention, module 203 may alsoinclude a Speaker Transfer Function (STF) module 206 to apply a STF topredicted noise signal 230, thereby to generate an output 249 having avalue corresponding to an estimation of noise destructive pattern 218produced in response to predicted noise signal 230. The STF may bedetermined, for example, based on characteristics of speaker 216, e.g.,as known in the art. STF module 206 may include any suitable hardwareand/or software, e.g., as are known in the art, to apply a predeterminedSTF to predicted noise signal 230. For example, the value, denoted z(n),of output 249 may be calculated using the following equation:

$\begin{matrix}{{z(n)} = {\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{y( {n - s} )}}}} & (2)\end{matrix}$

wherein S denotes a predetermined STF frequency parameter vector, as isknown in the art.

Substituting Equation 1 in Equation 2 may yield the following equation:

$\begin{matrix}{{z(n)} = {\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{\sum\limits_{k = 1}^{K}{w_{k}{\exp( {{- \frac{1}{2\;\upsilon_{k}}}{\sum\limits_{i = 0}^{L - 1}( {{x( {n - s - i} )} - {c_{k}(i)}} )^{2}}} )}}}}}} & (3)\end{matrix}$

According to exemplary embodiments of the invention, module 203 may alsoinclude a subtractor 208, which may be implemented by any suitablehardware and/or software as are known in the art. Subtractor 208 maysubtract the value of the estimated noise destructive pattern, e.g., ofoutput STF 249, from the estimated value of the noise pattern, e.g., ofoutput 241, to produce an output 245 including the evaluated noiseerror, denoted e(n), corresponding to sample MIC(n).

According to exemplary embodiments of the invention, estimator 202 mayimplement an adaptive algorithm to iteratively adapt the values of oneor more of the parameters v_(k), c_(k), and w_(k), e.g., based on thevalue of the noise error, as described below.

According to exemplary embodiments of the invention, the value of thenoise error e(n), corresponding to the n-th sample of noise signal 210may be estimated using the following equation:e(n)=d(n)−z(n)  (4)

Substituting Equation 3 in Equation 4 may yield the following equation:

$\begin{matrix}{{e(n)} = {{d(n)} - {\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{\sum\limits_{k = 1}^{K}{w_{k}{\exp( {{- \frac{1}{2\;\upsilon_{k}}}{\sum\limits_{i = 0}^{L - 1}( {{x( {n - s - i} )} - {c_{k}(i)}} )^{2}}} )}}}}}}} & (5)\end{matrix}$

According to some exemplary embodiments of the invention, estimator 202may iteratively adapt one or more of the parameters v_(k), c_(k), andw_(k), to reduce, e.g., minimize, the arithmetic mean, denotedE[(e(n))²], of the square of the noise error. For example, estimator 202may be able to iteratively adapt one or more of the parameters of theestimation function such that the partial derivative of E[(e(n))²] withrespect to one or more of the parameters, respectively, is equal tozero, as described below.

According to some exemplary embodiments of the invention, the arithmeticmean of the square of the estimated noise error may be calculated usingthe following equation:

$\begin{matrix}{{E\lbrack ( {e(n)} )^{2} \rbrack} = {E\lbrack ( {{d(n)} - {\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{\sum\limits_{k = 1}^{K}{w_{k}{f_{k}\lbrack {n - s} \rbrack}}}}}} )^{2} \rbrack}} & (6)\end{matrix}$wherein

$\begin{matrix}{{f_{k}\lbrack {n - s} \rbrack} = {\exp( {{- \frac{1}{2\;\upsilon_{k}}}{\sum\limits_{i = 0}^{L - 1}( {{x( {n - s - i} )} - {c_{k}(i)}} )^{2}}} )}} & (7)\end{matrix}$

The partial derivatives of Equation 6 with respect to the parametersc_(k), v_(k), and w_(k), respectively, may be calculated using thefollowing equations:

$\begin{matrix}{\frac{\partial{E\lbrack ( {e(n)} )^{2} \rbrack}}{\partial w_{k}} = {E\lbrack {{- 2}{e(n)}{\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{f_{k}\lbrack {n - s} \rbrack}}}} \rbrack}} & (8) \\{\frac{\partial{E\lbrack ( {e(n)} )^{2} \rbrack}}{\partial c_{k}} = {- {E\lbrack {2{e(n)}w_{k}{\sum\limits_{s = 0}^{S - 1}{{STF}(s){f_{k}\lbrack {n - s} \rbrack}( {\frac{1}{\upsilon_{k}}{\sum\limits_{i = 0}^{L - 1}( {{x( {n - i} )} - {c_{k}(i)}} )}} )}}} \rbrack}}} & (9) \\{\frac{\partial{E\lbrack ( {e(n)} )^{2} \rbrack}}{\partial\upsilon_{k}} = {E\lbrack {{e(n)}w_{k}{\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{f_{k}\lbrack {n - s} \rbrack}\frac{1}{( \upsilon_{k} )^{2}}{\sum\limits_{i = 0}^{L - 1}( {{x( {n - i} )} - {c_{k}(i)}} )^{2}}}}} \rbrack}} & (10)\end{matrix}$

A minimum value of E[(e(n))²]may be determined by from the followingequations:

$\begin{matrix}{\frac{\partial{E\lbrack ( {e(n)} )^{2} \rbrack}}{\partial w_{k}} = 0} & (11) \\{\frac{\partial{E\lbrack ( {e(n)} )^{2} \rbrack}}{\partial c_{k}} = 0} & (12) \\{\frac{\partial{E\lbrack ( {e(n)} )^{2} \rbrack}}{\partial\upsilon_{k}} = 0} & (13)\end{matrix}$

Applying the condition of Equation 11 to Equation 8 may result in thefollowing relation between an adapted value, denoted w_(k)(n+1), and thecurrent value, w_(k)(n), of the parameter w_(k):

$\begin{matrix}{{w_{k}( {n + 1} )} = {{w_{k}(n)} - {\mu_{w}{e(n)}{\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{f_{k}\lbrack {n - s} \rbrack}}}}}} & (14)\end{matrix}$wherein μ_(k) is a determined convergence parameter corresponding tow_(k).

Applying the condition of Equation 12 to Equation 9 may result in thefollowing relation between an adapted value, denoted c_(k)(n+1), and thecurrent value, c_(k)(n), of the parameter c_(k):

$\begin{matrix}{{c_{k}( {n + 1} )} = {{c_{k}(n)} - {\mu_{c}{e(n)}w_{k}{\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{f_{k}\lbrack {n - s} \rbrack}( {\frac{1}{\upsilon_{k}}{\sum\limits_{i = 0}^{L - 1}( {{x( {n - i} )} - {c_{k}(i)}} )}} )}}}}} & (15)\end{matrix}$wherein μ_(c) is a determined convergence parameter corresponding toc_(k).

Applying the condition of Equation 13 to Equation 10 may result in thefollowing relation between an adapted value, denoted v_(k)(n+1), and thecurrent value, v_(k)(n), of the parameter v_(k):

$\begin{matrix}{{\upsilon_{k}( {n + 1} )} = {{\upsilon_{k}(n)} - {\mu_{\upsilon}{e(n)}w_{k}{\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{f_{k}\lbrack {n - s} \rbrack}\frac{1}{( \upsilon_{k} )^{2}}{\sum\limits_{i = 0}^{L - 1}( {{x( {n - i} )} - {c_{k}(i)}} )^{2}}}}}}} & (16)\end{matrix}$wherein μ_(v) is a determined convergence parameter corresponding tov_(k).

According to some exemplary embodiments of the invention, adaptiveestimator 202 may implement one or more of Equations 14-16 toiteratively adapt one or more of the parameters w_(k), c_(k), and v_(k),respectively.

Some exemplary embodiments of the invention relate to an ANC system,e.g., system 100 (FIG. 1), implementing an error evaluation module,e.g., module 203, to evaluate the noise error at a predeterminedlocation, e.g., location 112 (FIG. 1). However, it will be appreciatedby those skilled in the art, that according to other embodiments of theinvention, any other one or more suitable modules may be implemented toevaluate the noise error. For example, an error-sensing microphone 239may be located at the predetermined location, and an output 240 oferror-sensing microphone 239 corresponding to the sensed noise error atthe predetermined location may be provided to estimator 202.

Some exemplary embodiments of the invention relate to an ANC system,e.g., ANC system 100 (FIG. 1), including a controller, e.g., controller106 (FIG. 1), to control an acoustic transducer, e.g., speaker 108 (FIG.1), based on a noise signal of a noise pattern received from an acousticsensor, e.g., microphone 102 (FIG. 1). However, other embodiments of theinvention may refer to an ANC system including a controller able tocontrol an acoustic transducer based on one or more noise signals of anoise pattern received from more than one acoustic sensor, e.g., asdescribed below.

Reference is made to FIG. 3, which schematically illustrates an ANCsystem 300 according to another exemplary embodiment of the invention.

ANC system 300 may include, for example, a primary acoustic sensor,e.g., a microphone 302, denoted MIC1, to sample the noise energy and/orwave amplitude of a noise pattern produced by a noise source 304.Microphone 302 may include any suitable microphone, e.g., as describedabove with reference to microphone 102 (FIG. 1).

ANC system 300 may also include an acoustic transducer, e.g., a speaker308, and a controller 306 able to control speaker 308 to produce a noisedestructive pattern to reduce or cancel the noise energy and/or waveamplitude of the noise pattern, e.g., within a reduced-noise zone 310,as described in detail below. Speaker 308 may include any suitablespeaker, e.g., as described above with reference to speaker 108 (FIG.1).

According to some exemplary embodiments of the invention, controller 306may be able to evaluate a noise error corresponding to a combination of,e.g., a difference between, the noise pattern and the noise destructivepattern, e.g., at a predetermined location 312 within zone 310, asdescribed below. Controller 306 may control speaker 308 to produce thenoise destructive pattern, for example, such that the noise error isreduced, e.g., minimized, as described below.

According to exemplary embodiments of the invention, a relatively goodcoherence between primary microphone 302 and the evaluation of the noiseerror, e.g., at the relevant frequencies of the noise pattern, may berequired in order for ANC 300 to achieve an efficient degree of noisereduction, as described below. For example, the higher correlationbetween the noise pattern sampled by microphone 302 and the noise error,the higher the level of noise control, e.g., noise reduction, which maybe achieved by ANC system 300. The coherence between the noise patternsampled by microphone 302 and the noise error may depend, for example,on the geometric structure of the path between microphone 302 andlocation 312. Additionally or alternatively, the coherence between thenoise pattern received by microphone 302 and the noise error may depend,for example, on the aerodynamic attributes, e.g., surface roughness, ofthe path. For example, no “eye contact” between microphone 302 andlocation 312 and/or a path having relatively rough surfaces may resultin a reduced coherence between the signal received by microphone 302 andthe evaluated noise error. Furthermore, the operation of ANC 300 toreduce the noise may be disturbed by formation of acoustic signals alongthe path between the microphone 302 and location 312, e.g., due toturbulent airflow and/or friction between the air and path materials,for example, if a structure of a device implementing one or moreelements of ANC 300 does not have an aerodynamically optimized design,e.g., due to price and size constraints. Turbulent airflow may becharacterized by stochastic formation of eddies which producesignificant rustles, and friction between the air and the relativelyrough surfaces may be characterized by conversion of kinetic energy intoheat and noise energy.

According to exemplary embodiments of the invention, the noise error maybe evaluated using a MTF, e.g., as described below with reference toFIG. 4. The MTF may be predetermined, e.g., based on one or morecharacteristics of the path between microphone 302 and location 312,and/or one or more expected characteristics of the noise-pattern.However, one or more of the characteristics of the path and/or theexpected characteristics of the noise pattern may be different than theexpected characteristics. As a result, the correlation between the noiseerror, e.g., evaluated based on the predetermined MTF, and the actualnoise at location 312 may not be sufficiently accurate.

According to some exemplary embodiments of the invention, ANC system 300may also include at least one secondary acoustic sensor, e.g., at leastone secondary microphone 392, denoted MIC21, to sample the noise energyand/or wave amplitude of the noise pattern produced by noise source 304.Secondary microphone 392 may be separated from noise source 304 by adistance, d1, bigger than the distance, d2, between primary microphone302 and noise source 304. For example, microphone 392 may be locatedalong the path between microphone 302 and location 312. The distanced1-d2 between microphone 392 and microphone 302 may be large enough toallow microphone 392 to sample a residual noise pattern, e.g., a noisepattern formed by the path, which may not be received by microphone 302.Microphone 392 may include any suitable microphone, e.g., as describedabove with reference to microphone 102 (FIG. 1).

According to some exemplary embodiments of the invention, controller 306may control speaker 308 to produce the noise destructive pattern basedon the noise pattern sensed by microphone 302 and/or the residual noisepattern sensed by microphone 392, as described below.

Reference is made to FIG. 4, which schematically illustrates acontroller 400 according to another exemplary embodiment of theinvention. Although the invention is not limited in this respect,controller 400 may be implemented by ANC system 300 (FIG. 3).

According to exemplary embodiments of the invention, controller 400 mayinclude a reference estimator 408 to receive from a primary microphone402 a primary noise signal 412, e.g., including a plurality of samples.Estimator 408 may generate a predicted primary signal 414 having avalue, y₁(n), corresponding to an n-th sample, denoted MIC1(n), receivedfrom microphone 402, by applying a primary estimation function F₁ to thesample MIC1(n) and to one or more other samples previously received frommicrophone 402, as described below.

According to exemplary embodiments of the invention, controller 400 mayalso include at least one secondary estimator 410 to receive from atleast one secondary microphone 404 at least one secondary noise signal,respectively, e.g., including a plurality of samples. Estimator 410 maygenerate a predicted secondary signal 422 having a value, y₂(n),corresponding to an n-th sample, denoted MIC21(n), received frommicrophone 404, by applying a secondary estimation function F₂ to thesample MIC21(n) and to one or more other samples previously receivedfrom microphone 404, as described below.

Controller 400 may control an acoustic transducer, e.g., a speaker 406,to generate a noise destructive pattern 418, e.g., based on acombination of signal 414 and signal 422. For example, controller 400may also include an adder 424, e.g., as is known in the art, to providespeaker 406 with an input 426 corresponding to the sum of signals 422and 414.

According to some exemplary embodiments of the invention, estimator 408may generate signal 414 according to the following equation:

$\begin{matrix}{{y_{1}(n)} = {\sum\limits_{s = 0}^{L_{1}}{{W_{1}(s)}{MIC}\; 1( {n - s} )}}} & (17)\end{matrix}$

wherein W₁ denotes a predetermined prediction filter (PF) vector oflength L₁ corresponding to estimation function F₁.

According to some exemplary embodiments of the invention, estimator 410may generate signal 422 according to the following equation:

$\begin{matrix}{{y_{2}(n)} = {\sum\limits_{s = 0}^{L_{2}}{{W_{2}(s)}{MIC}\; 21( {n - s} )}}} & (18)\end{matrix}$

wherein W₂ denotes a predetermined PF vector of length L₂ correspondingto estimation function F₂.

According to some exemplary embodiments of the invention, estimator 408may iteratively adapt the vector W₁, and/or estimator 410 mayiteratively adapt the vector W₂, e.g., based on a predeterminedcriterion, as described below.

According to some exemplary embodiments of the invention, estimator 408may iteratively adapt vector W₁, based on the noise error correspondingto the combination of, e.g., the difference between, the noise patternat the predetermined location, e.g., location 312 (FIG. 3), and anestimation of the contribution of signal y₁(n) to noise destructivepattern 418, as described below.

According to some exemplary embodiments of the invention, controller 400may also include a first evaluation module 430 to evaluate the noiseerror, e.g., based on signal 412 and signal 414, as described below.

According to some exemplary embodiments of the invention, module 430 mayinclude, for example, a combiner 434 to combine signals 412 and 420. Forexample, combiner 434 may include a first MTF module 436 to apply afirst predetermined MTF, denoted MTF₁, to signal 412 and to divide theresult by two. Combiner 434 may also include a second MTF module 438 toapply a second predetermined MTF, denoted MTF₂, to signal 420 and todivide the result by two. For example, MTF₁, may be determined, e.g., asknown in the art, based on characteristics of microphone 402 and/orbased on geometrical and/or physical characteristics of a path betweenmicrophone 412 and the certain location. MTF₂, may be determined, forexample, based on characteristics of microphone 404 and/or based ongeometrical and/or physical characteristics of a path between microphone404 and the predetermined location. Combiner 434 may also include anadder 440 to generate an output 442, denoted d(n), corresponding to anaverage between an estimation the n-th sample of the noise pattern atthe certain location using MTF₁, and an estimation the n-th sample ofthe noise pattern at the certain location using MTF₂.

For example, d(n) may be calculated using the following equation:

$\begin{matrix}{{d(n)} = {\frac{1}{2}( {{\sum\limits_{x = 0}^{M_{1}}( {{{MTF}_{1}(s)}{Mic}\; 1( {n - s} )} )} + {\sum\limits_{s = 0}^{M_{2}}( {{{MTF}_{2}(s)}{Mic}\; 21( {n - s} )} )}} )}} & (19)\end{matrix}$wherein M₁ denotes a predetermined number of samples of MTF₁, and M₂denotes a predetermined number of samples of MTF₂.

According to exemplary embodiments of the invention, module 430 may alsoinclude a STF module 450 to apply a STF to signal 414 to generate anoutput 452 representing an estimation of a primary part of the noisedestructive pattern corresponding to predicted primary signal 414. TheSTF may be determined, for example, based on characteristics of speaker406, e.g., as known in the art. STF module 450 may include any suitablehardware and/or software, e.g., as known in the art, to apply apredetermined STF to signal 414. For example, the value, denoted z₁(n),of output 452 may be calculated using the following equation:

$\begin{matrix}{{z_{1}(n)} = {\sum\limits_{p = 0}^{S - 1}{{{STF}(p)}{y_{1}( {n - p} )}}}} & (20)\end{matrix}$

Substituting Equation 17 in Equation 20 may yield the followingequation:

$\begin{matrix}{{z_{1}(n)} = {\sum\limits_{p = 0}^{S - 1}{{{STF}(p)}{\sum\limits_{s = 0}^{L_{1}}{{W_{1}(s)}{MIC}\; 1( {n - s - p} )}}}}} & (21)\end{matrix}$

According to exemplary embodiments of the invention, module 430 may alsoinclude a subtractor 454, e.g., implemented by any suitable hardwareand/or software as known in the art. Subtractor 454 may subtract thevalue of output 452, from the value of output 442, to produce an output455 including the evaluated noise error, denoted e₁(n), corresponding tosamples MIC1(n) and MIC21(n).

According to exemplary embodiments of the invention, estimator 408 mayimplement an adaptive algorithm to iteratively adapt the value of vectorW₁, e.g., based on the value of e₁(n), as described below.

According to exemplary embodiments of the invention, the noise error,e₁(n), corresponding to the n-th samples received from microphones 402and 404 may be evaluated using the following equation:e ₁(n)=d(n)−z ₁(n)  (22)

Substituting Equation 21 in Equation 22 may yield the followingequation:

$\begin{matrix}{{e_{1}(n)} = {{d(n)} - {\sum\limits_{p = 0}^{S - 1}{{{STF}(p)}{\sum\limits_{s = 0}^{L_{1}}{{W_{1}(s)}{MIC}\; 1( {n - s - p} )}}}}}} & (23)\end{matrix}$

According to some exemplary embodiments of the invention, estimator 408may iteratively adapt the value of vector W₁, to reduce, e.g., minimize,the evaluated noise error e₁(n). For example, estimator 408 may be ableto iteratively adapt the value of vector W₁ using the followingequation:

$\begin{matrix}{{W_{1}( {n + 1} )} = {{W_{1}(n)} - {\mu_{1}{\sum\limits_{s = 0}^{S - 1}{{{STF}(s)}{MIC}\; 1( {n - s} ){e_{1}(n)}}}}}} & (24)\end{matrix}$wherein W₁(n+1) denotes an adapted value of W₁, W₁(n) denotes thecurrent value of W₁, and μ₁ denotes a predetermined convergenceparameter corresponding to W₁. For example, μ₁ may be determinedaccording the following condition:

$\begin{matrix}{\mu_{1} < \frac{1}{2L_{1}}} & (25)\end{matrix}$

According to some exemplary embodiments of the invention, estimator 410may iteratively adapt the value of vector W₂ of the estimation functionF₂, based on an evaluated residual noise error corresponding to thecombination of, e.g., the difference between, the evaluated noise errore₁(n), and an estimation of the contribution of y₂(n) to noisedestructive pattern 418, as described below.

According to some exemplary embodiments of the invention, controller 400may also include at least one secondary evaluation module 432 toevaluate the residual noise error, e.g., based on signal 422 and theevaluated noise error e₁(n), as described below.

According to exemplary embodiments of the invention, module 432 mayinclude a STF module 460 to apply a STF to signal 422 to generate anoutput 462 representing an estimation of a secondary part of th noisedestructive pattern corresponding to signal 422. STF module 460 mayinclude any suitable hardware and/or software, e.g., as known in theart, to apply a predetermined STF to signal 422. The STF may bepredetermined, for example, based on characteristics of speaker 406,e.g., as known in the art. For example, the value, denoted z₂(n), ofoutput 462 may be calculated using the following equation:

$\begin{matrix}{{z_{2}(n)} = {\sum\limits_{p = 0}^{S - 1}{{{STF}(p)}{y_{2}( {n - p} )}}}} & (26)\end{matrix}$

Substituting Equation 18 in Equation 26 may yield the followingequation:

$\begin{matrix}{{z_{2}(n)} = {\sum\limits_{p = 0}^{S - 1}{{{STF}(p)}{\sum\limits_{x = 0}^{L_{2}}{{W_{2}(s)}{MIC}\; 21( {n - s - p} )}}}}} & (27)\end{matrix}$

According to exemplary embodiments of the invention, module 432 may alsoinclude a subtractor 464, e.g., implemented by any suitable hardwareand/or software as known in the art. Subtractor 464 may subtract thevalue of output 462, from the value of output 452, to produce an output466 including the evaluated residual noise error, denoted e₂(n),corresponding to samples MIC1(n) and MIC21(n).

According to exemplary embodiments of the invention, estimator 410 mayimplement an adaptive algorithm to iteratively adapt the value of vectorW₂, e.g., based on the value of e₂(n), as described below.

According to exemplary embodiments of the invention, the residual noiseerror, e₂(n), corresponding to the n-th samples received frommicrophones 402 and 404 may be evaluated using the following equation:e ₂(n)=e ₁(n)−z ₂(n)  (28)

Substituting Equations 23 and 27 in Equation 28 may yield the followingequation:

$\begin{matrix}{{e_{2}(n)} = {{d(n)} - {\sum\limits_{p = 0}^{S - 1}\;{{{STF}(p)}{\sum\limits_{s = 0}^{L_{1}}\;{{W_{1}(s)}{MIC}\; 1( {n - s - p} )}}}} - {\sum\limits_{p = 0}^{S - 1}\;{{{STF}(p)}{\sum\limits_{s = 0}^{L_{2}}\;{{W_{2}(s)}{MIC}\; 21( {n - s - p} )}}}}}} & (29)\end{matrix}$

According to some exemplary embodiments of the invention, estimator 410may iteratively adapt the value of vector W₂, to reduce, e.g., minimize,the evaluated residual noise error e₂(n). For example, estimator 410 maybe able to iteratively adapt one or more elements of vector W₁ using thefollowing equation:

$\begin{matrix}{{W_{2}( {n + 1} )} = {{W_{2}(n)} - {\mu_{2}{\sum\limits_{p = 0}^{S - 1}\;{{{STF}(p)}{MIC}\; 21( {n - s - p} ){e_{2}(n)}}}}}} & (30)\end{matrix}$wherein W₂(n+1) denotes an adapted value of W₂, W₂(n) denotes thecurrent value of W₂, and μ₂ denotes a predetermined convergenceparameter corresponding to W₂. For example, μ₂ may be determinedaccording the following condition:

$\begin{matrix}{\mu_{2} < \frac{1}{2L_{2}}} & (31)\end{matrix}$

Some of the embodiments described above may refer to ANC systemsimplementing a controller, e.g., controller 400, able to control anacoustic transducer, e.g., speaker 406, to generate a noise destructivepattern based on a combination of an a primary noise signal of a primaryacoustic sensor, e.g., microphone 402, and a secondary noise signal of asecondary acoustic sensor, e.g., microphone 404. However, it will beappreciated by those skilled in the art that according to otherembodiments of the invention, these systems may be modified to implementone or more additional secondary acoustic sensors. For example,controller 400 may be modified to include an additional plurality ofsecondary estimators to receive one or more primary noise signals of theone or more additional secondary microphones, respectively. For example,an i-th estimator of the additional secondary estimators may generate,for example, an output, denoted y_(i)(n), corresponding to the followingequation:

$\begin{matrix}{{y_{i}(n)} = {\sum\limits_{s = 0}^{L_{i}}\;{{W_{i}(s)}{{MICi}( {n - s} )}}}} & (32)\end{matrix}$wherein Wi denotes a predetermined prediction filter (PF) vector oflength L_(i) corresponding to the i-th estimator, and MICi denotes theoutput of the i-th additional secondary microphone.

Controller 400 may also be modified to include one or more additionalresidual noise error evaluators to evaluate a residual noise error,e.g., in analogy to evaluator 410. For example, an i-th residual errorevaluator may evaluate the i-th residual noise error, e_(i)(n), usingthe following equation:

$\begin{matrix}{{e_{i}(n)} = {{e_{i - 1}(n)} - {\sum\limits_{p = 0}^{S - 1}\;{{{STF}(p)}{\sum\limits_{s = 0}^{L_{i}}\;{{W_{i}(s)}{{MICi}( {n - s - p} )}}}}}}} & (33)\end{matrix}$

According to some exemplary embodiments, an i-th estimator of theadditional estimators may iteratively adapt the value of the vectorW_(i), e.g., using the following equation:

$\begin{matrix}{{W_{i}( {n + 1} )} = {{W_{i}(n)} - {\mu_{i}{\sum\limits_{s = 0}^{S - 1}\;{{{STF}(s)}{{MICi}( {n - s} )}_{i}}}}}} & (34)\end{matrix}$wherein W_(i)(n+1) denotes an adapted value of W_(i), W_(i)(in) denotesthe current value of W_(i), and μ_(i) denotes a predeterminedconvergence parameter corresponding to W_(i). For example, μ_(i) may bedetermined according the following condition:

$\begin{matrix}{\mu_{i} < \frac{1}{2L_{i}}} & (35)\end{matrix}$

Some of the embodiments described above may refer to ANC systemsimplementing a controller, e.g., controller 400, including one or moreestimators, e.g., estimators 408 and/or 410, to apply an adaptive linearestimation algorithm to one or more respective noise signals, e.g.,outputs 412 and/or 420. However, it will be appreciated by those skilledin the art that according to other embodiments of the invention, thesesystems may be modified to implement one or more estimators to apply anadaptive non-linear estimation algorithm to one or more respective noisesignals. For example, controller 400 may be modified to implement one ormore RBF estimation algorithms, e.g., in analogy to controller 200 (FIG.2).

Embodiments of the present invention may be implemented by software, byhardware, or by any combination of software and/or hardware as may besuitable for specific applications or in accordance with specific designrequirements. Embodiments of the present invention may include modules,units and sub-units, which may be separate of each other or combinedtogether, in whole or in part, and may be implemented using specific,multi-purpose or general processors, or devices as are known in the art.Some embodiments of the present invention may include buffers,registers, storage units and/or memory units, for temporary or long-termstorage of data and/or in order to facilitate the operation of aspecific embodiment.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents may occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

1. An active noise control system for controlling noise produced by anoise source, said system comprising: an acoustic sensor to sense anoise pattern and to produce a noise signal corresponding to the sensednoise pattern; an estimator to produce a predicted noise signal byapplying a non-liner estimation function to said noise signal, whereinthe predicted noise signal includes an estimation of a predicted sampleof the noise signal, which is successive to a current sample of thenoise signal, and wherein the estimator is to estimate the predictedsample by applying the estimation function to the current sample and toone or more samples preceding the current sample of the noise signal;and an acoustic transducer to produce a noise destructive pattern basedon said predicted noise signal, wherein the noise destructive patternhas a non-linear relationship to the noise pattern sensed by theacoustic sensor.
 2. The system of claim 1, wherein said estimator isable to adapt one or more parameters of said estimation function basedon a noise error at a predetermined location.
 3. The system of claim 2,wherein said noise error comprises an anticipated destructiveinterference between said noise pattern and said noise destructivepattern at said predetermined location.
 4. The system of claim 2comprising an error-sensing microphone to sense said noise error at saidpredetermined location.
 5. The system of claim 2 comprising an errorevaluator to evaluate said noise error based on said noise signal andsaid predicted noise signal.
 6. The system of claim 5, wherein saiderror evaluator comprises: a speaker transfer function module to producean estimation of said noise destructive pattern by applying a speakertransfer function to said predicted noise signal; a modulation transferfunction module to produce an estimation of said noise pattern at saidpredetermined location by applying a modulation transfer function tosaid noise signal; and a subtractor to subtract the estimation of saidnoise destructive pattern from the estimation of said noise pattern. 7.The system of claim 2, wherein said estimator is able to adapt said oneor more parameters based on a predetermined criterion.
 8. The system ofany one of claim 7, wherein said estimator is able to reduce said errorvalue by adapting said one or more parameters.
 9. The system of claim 8,wherein said adaptive estimator is able to minimize said error value byadapting said one or more parameters.
 10. The system of claim 2, whereinsaid one or more parameters comprise at least one parameter selectedfrom the group consisting of a center parameter, an effective radiusparameter, and an intensity parameter.
 11. The system of claim 10,wherein said estimator is able to adapt said center parameter based onthe following equation:${c_{k}( {n + 1} )} = {{c_{k}(n)} - {\mu_{c}{e(n)}w_{k}{\sum\limits_{s = 0}^{S - 1}\;{{{STF}(s)}{f_{k}\lbrack {n - s} \rbrack}( {\frac{1}{\upsilon_{k}}{\sum\limits_{i = 0}^{L - 1}\;( {{x( {n - i} )} - {c_{k}(i)}} )}} )}}}}$wherein c_(k)(n+1) denotes an adapted value of said center parameter,c_(k)(n) denotes a current value of said center parameter, w_(k) denotessaid intensity parameter, L denotes a predetermined number of samples ofsaid noise signal, STF denotes a predetermined speaker transferfunction, S denotes a predetermined speaker transfer function frequencyparameter, μ_(c) denotes a predetermined convergence parametercorresponding to said center parameter, v_(k) denotes said effectiveradius parameter, e(n) denotes said noise error, f_(k) denotes apredetermined function, and x(n) denotes an n-th sample of said noisesignal.
 12. The system of claim 10, wherein said estimator is able toadapt said effective radius parameter based on the following equation:${\upsilon_{k}( {n + 1} )} = {{\upsilon_{k}(n)} - {\mu_{\upsilon}{e(n)}w_{k}{\sum\limits_{s = 0}^{S - 1}\;{{{STF}(s)}{f_{k}\lbrack {n - s} \rbrack}\frac{1}{( \upsilon_{k} )^{2}}{\sum\limits_{i = 0}^{L - 1}\;( {{x( {n - i} )} - {c_{k}(i)}} )^{2}}}}}}$wherein v_(k)(n+1) denotes an adapted value of said effective radiusparameter, v_(k)(n) denotes a current value of said effective radiusparameter, w_(k) denotes said intensity parameter, L denotes apredetermined number of samples of said noise signal, STF denotes apredetermined speaker transfer function, S denotes a predeterminedspeaker transfer function frequency parameter, μ_(v), denotes apredetermined convergence parameter corresponding to said effectiveradius parameter, c_(k) denotes said center parameter, e(n) denotes saidnoise error, f_(k) denotes a predetermined function, and x(n) denotes ann-th sample of said noise signal.
 13. The system of claim 10, whereinsaid estimator is able to adapt said intensity parameter based on thefollowing equation:${w_{k}( {n + 1} )} = {{w_{k}(n)} - {\mu_{w}{e(n)}{\sum\limits_{s = 0}^{S - 1}\;{{{STF}(s)}{f_{k}\lbrack {n - s} \rbrack}}}}}$wherein w_(k)(n+1) denotes an adapted value of said intensity parameter,w_(k)(n) denotes a current value of said intensity parameter, w_(k)denotes said intensity parameter, L denotes a predetermined number ofsamples of said noise signal, STF denotes a predetermined speakertransfer function, S denotes a predetermined speaker transfer functionfrequency parameter, μ_(w), denotes a predetermined convergenceparameter corresponding to said intensity parameter, f_(k) denotes apredetermined function, and x(n) denotes an n-th sample of said noisesignal.
 14. The system of claim 1, wherein said estimation functioncomprises a non-linear estimation function, wherein the estimator isable to estimate a noise error corresponding to an anticipateddestructive interference between a pattern of the noise and the noisedestructive pattern at a predetermined location, wherein saidpredetermined location is distinct from a location of said acousticsensor.
 15. The system of claim 14, wherein said non-linear functioncomprises a radial basis function.
 16. The system of claim 1, whereinsaid acoustic sensor comprises a microphone, and wherein the noisedestructive pattern produced by the acoustic transducer has anexponential relationship to the noise pattern sensed by the acousticsensor.
 17. The system of claim 1, wherein said acoustic transducercomprises a speaker, wherein said acoustic sensor comprises an array oftwo or more microphones, wherein the two or more microphones are locatedin two or more, respective, locations, wherein the two or moremicrophones are adapted to achieve coherence between the sensed noisepattern and the noise produced by the noise source, by taking intoaccount at least one or more of: geometric structure of a path betweensaid microphones and the noise source; aerodynamic attributes of thepath between said microphones and the noise source; surface roughnessalong the path between said microphones and the noise source; turbulentairflow along the path between said microphones and the noise source;formation of acoustic signals along the path between said microphonesand the noise source.
 18. An active noise control system for controllinga noise produced by a noise source, said system comprising: a primaryacoustic sensor to sense a noise pattern and to produce a correspondingprimary noise signal; at least one secondary acoustic sensor to sense aresidual noise pattern and to produce at least one secondary noisesignal corresponding to the residual noise pattern sensed by said atleast one secondary acoustic sensor, respectively, wherein said at leastone secondary acoustic sensor is separated from said noise source by adistance larger than a distance between said primary acoustic sensor andsaid noise source; and a controller functionally associated with anacoustic transducer and at least one estimator to produce a predictednoise signal, wherein the predicted noise signal includes an estimationof a predicted sample of at least one sampled signal of the primarynoise signal and the secondary noise signal, which is successive to acurrent sample of the sampled signal, and wherein the estimator is toestimate the predicted sample by applying at least one non-linearestimation function to the current sample and to one or more samplespreceding the current sample of the sampled signal, wherein saidcontroller is adapted to produce a noise destructive pattern based onsaid primary noise signal and said at least one secondary noise signaland said predicted noise signal, and wherein the noise destructivepattern produced by the controller has a non-linear relationship to thenoise pattern sensed by the primary acoustic sensor.
 19. The system ofclaim 18, wherein said at least one estimator includes a primaryestimator adapted to produce a predicted primary signal by applying aprimary estimation function to said primary noise signal and at leastone secondary estimator to produce at least one predicted secondarysignal by applying at least one secondary estimation function to said atleast one secondary noise signal, respectively.
 20. The system of claim19, wherein said primary estimator is able to iteratively adapt one ormore parameters of said primary estimation function based on a noiseerror.
 21. The system of claim 19, wherein said at least one secondaryestimator is able to iteratively adapt one or more parameters of said atleast one secondary estimation function, respectively, based on a noiseerror.
 22. The system claim 19, wherein said controller is able tocontrol said acoustic transducer based on a combination of saidpredicted primary signal and said at least one predicted secondarysignal.
 23. The system of claim 22, wherein said controller is able tocontrol said acoustic transducer based on the sum of said predictedprimary signal and said at least one predicted secondary signal.
 24. Thesystem claim 20, wherein said controller comprises a noise errorevaluator to evaluate a noise error corresponding to an anticipateddestructive interference between a pattern of the noise and the noisedestructive pattern at a predetermined location, wherein saidpredetermined location is distinct from locations of said primary andsecondary acoustic sensors.
 25. The system of claim 24, wherein saidnoise error evaluator is able to evaluate said noise error based on saidprimary noise signal, said at least one secondary noise signal and saidpredicted primary signal.
 26. The system of claim 25, wherein said noiseerror evaluator comprises: a speaker transfer function module to producean estimation of a primary part of said noise destructive patterncorresponding to said predicted primary signal by applying a speakertransfer function to said predicted primary signal; a modulationtransfer function module to produce an estimation of said noise patternby applying a modulation transfer function to a combination of saidprimary noise signal and said at least one secondary noise signal; and asubtractor to subtract the estimation of the primary part of said noisedestructive pattern from the estimation of said noise pattern.
 27. Thesystem of claim 24, wherein said controller comprises at least oneresidual noise evaluator to evaluate at least one residual noise. 28.The system of claim 27, wherein said at least one residual noiseevaluator is able to evaluate said residual noise based on said noiseerror and said at least one predicted secondary signal, respectively.29. The system of claim 28, wherein said residual error evaluatorcomprises: a speaker transfer function module to produce an estimationof a secondary part of said noise destructive pattern corresponding tosaid predicted secondary signal by applying a speaker transfer functionto said predicted secondary signal; and a subtractor to subtract theestimation of the secondary part of said noise destructive pattern fromsaid noise error.
 30. The system of claim 18, wherein at least one ofsaid primary acoustic sensor and said at least one secondary acousticsensor comprises a microphone, and wherein the noise destructive patternproduced by the acoustic transducer has an exponential relationship tothe noise pattern sensed by the primary acoustic sensor.
 31. The systemof claim 18, wherein said acoustic transducer comprises a speaker,wherein said primary acoustic sensor comprises an array of two or moremicrophones, wherein the two or more microphones are located in two ormore, respective, locations, wherein the two or more microphones areadapted to achieve coherence between the sensed noise pattern and thenoise produced by the noise source, by taking into account at least oneor more of: geometric structure of a path between said microphones andthe noise source; aerodynamic attributes of the path between saidmicrophones and the noise source; surface roughness along the pathbetween said microphones and the noise source; turbulent airflow alongthe path between said microphones and the noise source; formation ofacoustic signals along the path between said microphones and the noisesource.